Applying Fuzzy Fault Tree Method to Evaluate the Reliability of College Classroom Teaching

Wang, Liangliang; Fan, Mingfang; Zhang, Feng

doi:10.3389/fpsyg.2021.593068

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Therefore, the quality evaluation of online teaching in colleges and universities can not only supervise the professional construction of colleges and universities in the special period but can also provide references and suggestions for the development of online teaching pattern. On the basis of combing the literature related to classroom teaching [62][63][64] and teaching satisfaction [6,65,66], combined with the features of online teaching, 4 criteria levels and 14 attribute levels that affect the quality of online teaching, such as teacher factors, student factors, classroom factors and technical factors, are extracted to form an online teaching quality evaluation index system. The details are shown in the following Table 1.…”

Section: Construction Of the Online Teaching Quality Evaluation Index...mentioning

confidence: 99%

Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator

Zeng

Pan

Jin

2022

Systems

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Due to the full-scale outbreak of COVID-19, many universities have adopted the way of online teaching to ensure the orderly development of teaching plans and teaching contents. However, whether online and offline teaching can develop homogeneously and how to ensure the teaching effect is a major challenge for colleges and universities. Therefore, it is urgent to construct a reasonable index system and evaluation approach for the quality of network teaching. Combined with the influencing factors and characteristics of online teaching, this study first puts forward a multi-index evaluation index system and then proposes a novel evaluation method for online teaching based on the analytical hierarchy process (AHP) and Dombi weighted partitioned Muirhead Mean (PMM) operator under Fermatean fuzzy (FF) environment. This presented method not only adapts to changeable evaluation information but also handles the elusive interrelationships among indexes, realizing the flexibility and comprehensiveness both in form and in the polyaddition process. The applicability and feasibility of this presented method are then discussed through the practical online teaching quality evaluation of a business statistics course case, and a group of tentative about the sensitivity analysis and comparative analysis further demonstrates the effectiveness and flexibility of the proposed method.

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Section: Construction Of the Online Teaching Quality Evaluation Index...mentioning

confidence: 99%

Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator

Zeng

Pan

Jin

2022

Systems

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A Data-Driven Optimization Model of Important Multidimensional Factors Affecting College Students’ Cognitive Engagement in Ideological and Political Theory Course

Wang

2021

Scientific Programming

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The optimization of important multidimensional factors is conducive to cognitive engagement, which is a crucial dimension of student engagement and plays a significant role in college students’ learning of the Ideological and Political Theory Course. However, because there are many influencing factors associated with cognitive engagement, the influence mechanism and analysis strategy of this kind of model are relatively complex. In order to solve this research gap, this paper establishes an optimization model affecting Chinese college students’ cognitive engagement in IPTC on the basis of sample collection and investigation. In this process, 4,700 questionnaires were distributed to 47 colleges and universities across the country, and copies were effectively recovered (N = 3992); the effective recovery rate was 84.94%. Cronbach’s alpha of 0.759 indicates that the scale has high reliability and Pearson’s correlation coefficient P ≤ 0.001 shows that the scale has good validity. The KMO value of 0.703 in the Bartlett sphere test also shows that the scale is suitable for factor analysis. Firstly, according to the method of factor analysis, there are six important factor dimensions affecting college students’ cognitive engagement in the IPTC, namely, attention and motivation factor dimension, behavior and value attainment factor dimension, interest and practicality factor dimension, personality and will factor dimension, evaluation and time factor dimension, and knowledge and strategy factor dimension. Then, through descriptive analysis, it is found that personality and will factor dimension (M = 6.5837) plays a relatively major role while knowledge motivation dimension (M = 6.3505) has a weak impact on cognitive engagement. Finally, from linear regression analysis, there is a significant positive correlation between cognitive engagement and other variables. In addition, undergraduates are slightly lacking motivation in the learning of the course, and vigorously strengthening college students’ cognitive engagement is still necessary, so as to effectively enhance the effectiveness of the IPTC in the future.

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China’s higher education development evaluation based on GA-BP neural network

Ren

Wang

2022

JCM

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The development of higher education supplies a large number of high-level talents to the society, which is the key to building a harmonious society. At present, the development of regional higher education is extremely uneven, and it is the top priority of education development that it is urgent to clarify the situation of regional higher education. This article constructs a comprehensive evaluation index system of higher education development from a total of 19 indicators from five dimensions of talent training, teacher strength, scientific research output, infrastructure and social services, and then uses entropy and genetic algorithm-projection pursuit model to calculate the weight. GA-BP and BP neural network models are used for comprehensive evaluation. It is found that: (1) The most important factors affecting the development of higher education are technology transfer income and the application of R&D achievements in colleges and universities; (2) Compared with BP neural network, GA optimizes BP neural network in terms of effectiveness, convergence speed, and accuracy. (3) Generally speaking, during the research period, the development of China’s higher education has gradually improved, with an average annual growth rate of 3.5%. In terms of sub-regions, the provinces with excellent higher education development levels have increased from 0 in 2008. The number has increased to 5 in 2019, and the development of higher education among provinces is extremely uneven, and the differences between provinces are gradually increasing.

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Applying Fuzzy Fault Tree Method to Evaluate the Reliability of College Classroom Teaching

Cited by 4 publications

References 32 publications

Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator

Online Teaching Quality Evaluation of Business Statistics Course Utilizing Fermatean Fuzzy Analytical Hierarchy Process with Aggregation Operator

A Data-Driven Optimization Model of Important Multidimensional Factors Affecting College Students’ Cognitive Engagement in Ideological and Political Theory Course

China’s higher education development evaluation based on GA-BP neural network

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